An accurate iris segmentation method is crucial for the iris recognition system. The conventional iris segmentation algorithms have poor adaptability when applied to heterogeneous iris databases. Therefore, researchers have applied deep learning to the field of iris segmentation. A modified U-Net is proposed to perform the iris segmentation for heterogeneous iris databases, referred to as DropBlock and modified shortcut branch U-Net. The main work is as follows: first, EfficientNetV2 based on DropBlock is used as convolutional blocks of U-Net to improve the ability of feature extraction and generalization of the network. Second, an improved shortcut branch structure is proposed for U-Net to reduce the loss of information during the downsampling process. The experimental results on the CASIA-iris-interval-v4, IITD, and UBIRIS.v2 iris databases demonstrate that this method can not only have good versatility but also provide higher accuracy on heterogeneous databases. Compared with some state-of-the-art fusion methods, the proposed method has a significant performance advantage.

Iris segmentation is a critical step in the iris recognition system. Since the quality of iris database taken under different camera sensors varies greatly, thus most existing iris segmentation methods are designed for a particular collection device. Meanwhile, many iris segmentation methods based on convolutional neural networks (CNNs) require a lot of computational costs and hardware costs (storage space), which are not suitable for deploying on low‐performance devices. To address the above problems, an accurate and efficient heterogeneous iris segmentation network is proposed in this paper. First, the authors design an efficient feature extraction network, which combines depth‐wise separable convolution with traditional convolution to greatly reduce model parameters and computational cost while maintaining segmentation accuracy. Then, a Multi‐scale Context Information Extraction Module (MCIEM) is proposed to extract multi‐scale spatial information at a more granular level and enhance the discriminability of the iris region. Finally, a Multi‐layer Feature Information Fusion Module (MFIFM) is proposed to reduce the loss of information during the downsampling process. Experimental results on multi‐source heterogeneous iris database show that the proposed network can not only achieve state‐of‐the‐art performance but is also more efficient in terms of required parameters, calculated load, and storage space.

Iris segmentation is an important task in the iris recognition system. Traditional iris segmentation methods are susceptible to irrelevant noise. At present, segmentation methods based on convolutional neural networks (CNNs) have become the main trend in addressing the task. Although these networks achieve promising segmentation performance on the advanced graphics processor, they require a lot of computation and time, which are far from meeting the standards of real-time iris segmentation. To this end, a precise and fast iris segmentation algorithm is proposed. First, an efficient feature extraction network that combines depth-wise separable convolution with dilated convolution is designed to reduce model parameters while maintaining segmentation accuracy. Then, an attention mechanism is introduced to suppress noise interference and enhance the discriminability of the iris region. Finally, an auxiliary training branch is proposed to overcome the vanishing gradient problem. Experimental results show that the proposed method not only achieves state-of-the-art performance but also is more efficient in terms of required parameters, calculated load, and storage space. Specifically, the model parameter is only 0.1 million (M), and the average prediction time is <0.03 seconds (S).

Iris recognition is used in applications of security, national defense, e-commerce, and mobile devices due to its convenience, stability, uniqueness, better anticounterfeiting, etc.^1,2 The iris recognition system includes iris image acquisition, preprocessing, iris segmentation and localization, iris normalization, iris feature extraction, and iris matching verification or identification. In an accurate iris segmentation method, the iris region includes more discriminative features and gets a higher final recognition accuracy.³ With the development of image processing technology, biometric verification products based on the iris have been gradually deployed on low-power hardware. However, many challenges remain in this line of research. The factors that render iris segmentation on low-power devices a challenging task can be summarized as follows: